Data analysis methods

In line with the mixed methods approach applied in this current study, data analysis techniques are detailed under two headings below 1) quantitative data analysis techniques and 2) qualitative data analysis techniques.

3.9.1 Quantitative data analysis techniques

Choosing the appropriate data analysis techniques is one of the most important processes when conducting research. The techniques used must be well-matched to the research design and able to provide answers to the research questions. For quantitative data methods, statistical techniques can be classified into three approaches: univariate, bivariate and multivariate, depending on the number of variables available in the data (e.g. one, two, or more variables).

Univariate techniques are used in order to describe the statistical aspects of a single variable. In the current research, these statistical techniques are employed to measure

81 and present results such as the median, mean, standard deviation, and percentages in relation to the demographic profile of respondents.

To analyse the relationships between two variables, bivariate approaches are the appropriate tools to employ. Pearson’s Product Moment Correlation Coefficient was used to indicate the value of correlation between two variables. This value can range from -1.00 to 1.00. When the value is close to 0, it indicates that the variables are statistically significantly different (Pallant 2007).

The selection of statistical instruments depends on several criteria such as the nature of criterion variable (e.g. ordinal or interval), number of comparison groups (e.g. two or more), and the correlation between comparison groups such as relevant or irrelevant (Pallant 2007). Bivariate analysis techniques used for this research related to the statistical analyses employed consisting of T-tests and the Mann-Whitney U-test (Pallant 2007). These were applied to compare the relationship between groups of recent staff members from selected universities and their perceptions of website components in relation to which attributes attracted them to apply for a position in the university.

To analyse data relating to more than two variables, multivariate techniques are required. For the current research, a one-way analysis of variance was conducted to compare the variance across the three groups of ranked universities (e.g. Top, Middle, and Lower ranked groups) in relation to which university website components affect potential candidate perceptions and lead them to apply for a job in the university. In addition, a factor analysis was employed to identify interrelationships among a set of website components. By reviewing related literature, the website components used were selected as having been found to be related to factors found to affect candidate perceptions and influence their decision to apply for a job. Factor analysis was used to measure the correlation between each of the variables and to ensure correct categorisation of selected components.

82 3.9.2 Qualitative data analysis techniques

A semi-structured interview method was conducted in this study to extend the findings related to how universities efficiently use their websites in order to attract potential applicants and retain current staff. The impact of a university’s website on staff attraction and retention is one of the significant issues being focused on this study; therefore, it was the basis of main question in the interviews. All conversations were recorded, as permission to do so was provided by all interview participants. The tape recordings were then transcribed ‘word-for-word’ and content analysis was then used in this study to identify themes.

Holsti (1962) defined a content analysis technique as the systematic analysis of content to identify specific meaning of messages. By using content analysis, written text can be transformed into highly reliable data for further analysis purposes (Singleton Jr., Straits & Straits 1993). The main objective of employing content analysis in the current research was to identify a set of categories in relation to university websites, their impact on attraction potential academic staff and on retention of current staff in Thai universities.

First the recorded interviews were transcribed to obtain a general understanding of the data. All concepts, themes, and issues were then identified to create broad categories and remove unrelated or repeated information from the raw interview transcripts. Next, the broad categories were combined having regard to the similarity of the concepts and related themes to create more specific categories. Once the specific categories had been produced, the data was again reviewed to ensure the validity of the categorisation process.

3.10 Summary

This chapter has presented the research methodology employed in the current research in order to answer the research questions. It began with a justification of research procedure and overview of the research methodology and research methods used to conduct this research. The conceptual framework was presented from which the research questions and hypotheses were developed. Then, the research design was

83 presented, instrument development was described, and ethical considerations were discussed. The data gathering process and data analysis techniques were discussed and described. The research results are presented in the following chapter.

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Chapter 4

Data Analysis and Research Results

4.1 Introduction

The research methodology applied to gather and analyse data for the current research was described and justified in the previous chapter. This chapter provides the results of both quantitative and qualitative analyses conducted.

This chapter encompasses four main sections. The first section serves as an introduction presenting the demographic profile of respondents. After collecting data from the questionnaires, data needed to be prepared before further analysis. The second section describes the data preparation process. The third section reports the findings in relation to university websites. Data was analysed using the SPSS program version 18.0. The last section presents the results of analysis of qualitative data from the interviews.

SECTION ONE: